Network and customer experience monitoring solutions are widely accepted standards for the operations of carrier service provider networks across both fixed networks (e.g., Cable/MSO, IP broadband such as DSL, FTTH, etc.) and mobile networks (e.g., 2.5G, 3G, LTE, etc.). These systems monitor network traffic via probe devices, then process that traffic through a variety of stages to derive actionable information as it pertains to subscriber experience (quality of service, quality of experience), subscriber behavior (application usage, service usage, etc.), subscriber location, etc. In practice, actionable information may refer to statistical indicators (typically referred to as Key Performance Indicators or KPIs) that are computed from source data processed by the probes, and then made available to various different user constituents at the carrier for the purpose of driving their business process.
A few examples of KPIs include Handover Success (by node, location, etc.), Call Drop Ratio (by node, handset, etc.), Application Usage (by node, subscriber, etc.), Subscriber Count (by location, demographic, etc.), and the like.
As the inventor hereof has recognized, there are multiple macro-level drivers present in the market today that impact the Carrier Service Providers (CSPs) in ways that may affect their deployment and usage of monitoring systems and KPIs. For example, because of downward pressure on subscriber growth, subscriber Average Revenue Per User (ARPU), growing network complexity, etc., CSPs must continually improve operational efficiency. A major way CSPs improve efficiency is by increased reliance on KPIs that embed directly into business processes and automation. That is, CSPs increasingly rely on accurate data to make real-time operational decisions about activity on the network. Also, there is an increasing push for CSPs to leverage data present on their networks to enable new revenue streams. A few examples include using subscriber behavior data to better target additional CSP service offerings, packaging aggregated data about subscriber interests and behaviors to third party advertisers, etc.
Taken together, these drivers mean the following: availability and accuracy of KPIs are more important than ever because KPIs obtained from monitoring systems are increasingly going to trigger network, business, and potentially revenue impacting decisions. As such, the inventor hereof has identified a need for systems and methods that provide the ability to present users with a confidence interval for a given KPI so that they can more fully appreciate the significance of a metric before they take a network or business impacting action. As the inventor hereof has also recognized, however, existing KPI measuring solutions assume that all data is monitored, and that all monitored data is taken into account for KPI calculations. While there are other solutions that use a sampling approach, those systems use a fixed global sampling ratio, which renders the extrapolation of KPIs from observed data a relatively futile exercise.